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Thursday, February 25, 2021

The Economic Burden of Postoperative Complications Predicted by the Comprehensive Complication Index® in Patients Undergoing Elective Major Hepatopancreaticobiliary Surgery for Malignancy - A Prospective Cost Analysis

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In Vivo. 2021 Mar-Apr;35(2):1065-1071. doi: 10.21873/invivo.12351.

ABSTRACT

BACKGROUND/AIM: Postoperative complications have a tremendous impact on in-hospital costs. The novel Comprehensive Complication Index® (CCI®) summarizes all complications together and is more sensitive than existing morbidity endpoints. The purpose of this study was to assess the correlation of CCI® with in-hospital costs and externally validate a novel cost prediction calculator.

PATIENTS AND METHODS: This was a prospective study including consecutive patients undergoing elective major hepatopancreaticobiliary (HPB) surgery for malignancy at a London tertiary referral hospital. A priori sample size and post-hoc power calculations were performed.

RESULTS: Thirty patients were included in the analysis, 14 were female, and the median age was 67 [interquartile range (IQR)=54-74] years. The median Charlson Comorbidity Index was 6 (IQR=5-8). Eighteen patients underwent liver, 9 pancreatic surgery and three a palliative bypass; 11 patients had a major complication (≥grade 3a) according to the Clavien-Dindo classification. The median CCI® was 30.2 (IQR=12.18-39.5). The mean cost per case was 13,908 (SD=4,600) GBP. There was no correlation between the Charlson Comorbidity Index or age with actual cost. However, there was very good correlation of actual cost with the CCI® (r=0.77, 95% confidence interval=0.57-0.89, p<0.001) as well as with the predicted cost (Clavien Cost Prediction Calculator) (r=0.70, 95% confidence interval=0.44-0.85, p<0.001).

CONCLUSION: These findings support the hypothesis that complications are the most important predictor of overall cost in the setting of elective major HPB surgery for malignancy. Furthermore, CCI® and the novel Cost Prediction Calculator can be used in this setting to accurately predict costs using no additi onal resources.

PMID:33622903 | DOI:10.21873/invivo.12351

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